Misuse Detection for Mobile Devices Using Behaviour Profiling

2011 ◽  
Vol 1 (1) ◽  
pp. 41-53 ◽  
Author(s):  
Fudong Li ◽  
Nathan Clarke ◽  
Maria Papadaki ◽  
Paul Dowland
Keyword(s):  
Mobile Devices ◽  
Error Rate ◽  
Text Message ◽  
Modern Society ◽  
Experimental Results ◽  
Equal Error Rate ◽  
Misuse Detection ◽  
General Application ◽  
Point Of Entry ◽  
Application Specific

Mobile devices have become essential to modern society; however, as their popularity has grown, so has the requirement to ensure devices remain secure. This paper proposes a behaviour-based profiling technique using a mobile user’s application usage to detect abnormal activities. Through operating transparently to the user, the approach offers significant advantages over traditional point-of-entry authentication and can provide continuous protection. The experiment employed the MIT Reality dataset and a total of 45,529 log entries. Four experiments were devised based on an application-level dataset containing the general application; two application-specific datasets combined with telephony and text message data; and a combined dataset that included both application-level and application-specific. Based on the experiments, a user’s profile was built using either static or dynamic profiles and the best experimental results for the application-level applications, telephone, text message, and multi-instance applications were an EER (Equal Error Rate) of 13.5%, 5.4%, 2.2%, and 10%, respectively.

scholarly journals Combining Cryptography with EEG Biometrics

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Robertas Damaševičius ◽  
Rytis Maskeliūnas ◽  
Egidijus Kazanavičius ◽  
Marcin Woźniak
Keyword(s):  
Error Rate ◽  
User Authentication ◽  
Discrete Logarithm ◽  
Security Analysis ◽  
Experimental Results ◽  
Biometric Authentication ◽  
Equal Error Rate ◽  
Eeg Data ◽  
Authentication System ◽  
Biometric Cryptosystem

Cryptographic frameworks depend on key sharing for ensuring security of data. While the keys in cryptographic frameworks must be correctly reproducible and not unequivocally connected to the identity of a user, in biometric frameworks this is different. Joining cryptography techniques with biometrics can solve these issues. We present a biometric authentication method based on the discrete logarithm problem and Bose-Chaudhuri-Hocquenghem (BCH) codes, perform its security analysis, and demonstrate its security characteristics. We evaluate a biometric cryptosystem using our own dataset of electroencephalography (EEG) data collected from 42 subjects. The experimental results show that the described biometric user authentication system is effective, achieving an Equal Error Rate (ERR) of 0.024.

Palm Vein Recognition Algorithm Using Curvelet and Wavelet

2013 ◽  
Vol 710 ◽  
pp. 655-659
Author(s):  
Zhi Xian Jiu ◽  
Qiang Li
Keyword(s):  
Wavelet Transform ◽  
Error Rate ◽  
Minimum Distance ◽  
Image Database ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Experimental Results ◽  
Equal Error Rate ◽  
Vein Recognition ◽  
Palm Vein

In this paper we report on a curvelet and wavelet based palm vein recognition algorithm. Using our palm vein image database, we employed minimum distance classifier to test the performance of the system. Experimental results show that the algorithm based on cuvelet transform can reach equal error rate of 1.7%, and the algorithm based on wavelet transform can only reach equal error rate of 2.3%, indicating that the curvelet based palm vein recognition system improves representation.

A Preliminary Study on Non-Intrusive User Authentication Method Using Smartphone Sensors

2013 ◽  
Vol 284-287 ◽  
pp. 3270-3274 ◽  
Author(s):  
Chien Cheng Lin ◽  
Chin Chun Chang ◽  
De Ron Liang ◽  
Ching Han Yang
Keyword(s):  
Error Rate ◽  
User Authentication ◽  
Experimental Results ◽  
Equal Error Rate ◽  
Behavioral Biometrics ◽  
Reported Study ◽  
Smartphone Sensors ◽  
Preliminary Study ◽  
Orientation Sensor

This paper proposes a non-intrusive authentication method based on two sensitive apparatus of smartphones, namely, the orientation sensor and the touchscreen. We have found that these two sensors are capable of capturing behavioral biometrics of a user while the user is engaged in relatively stationary activities. The experimental results with respect to two types of flick operating have an equal error rate of about 3.5% and 5%, respectively. To the best of our knowledge, this work is the first publicly reported study that simultaneously adopts the orientation sensor and the touchscreen to build an authentication model for smartphone users. Finally, we show that the proposed approach can be used together with existing intrusive mechanisms, such as password and/or fingerprints, to build a more robust authentication framework for smartphone users.

scholarly journals Speaker Verification Employing Combinations of Self-Attention Mechanisms

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2201
Author(s):  
Ara Bae ◽  
Wooil Kim
Keyword(s):  
Error Rate ◽  
Speaker Recognition ◽  
Speaker Verification ◽  
Recognition Performance ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Combination Method ◽  
Noisy Environments ◽  
Equal Error Rate ◽  
Verification System

One of the most recent speaker recognition methods that demonstrates outstanding performance in noisy environments involves extracting the speaker embedding using attention mechanism instead of average or statistics pooling. In the attention method, the speaker recognition performance is improved by employing multiple heads rather than a single head. In this paper, we propose advanced methods to extract a new embedding by compensating for the disadvantages of the single-head and multi-head attention methods. The combination method comprising single-head and split-based multi-head attentions shows a 5.39% Equal Error Rate (EER). When the single-head and projection-based multi-head attention methods are combined, the speaker recognition performance improves by 4.45%, which is the best performance in this work. Our experimental results demonstrate that the attention mechanism reflects the speaker’s properties more effectively than average or statistics pooling, and the speaker verification system could be further improved by employing combinations of different attention techniques.

SenCS

2021 ◽  
Vol 17 (2) ◽  
pp. 1-22
Author(s):  
Chaohao Li ◽  
Xiaoyu Ji ◽  
Bin Wang ◽  
Kai Wang ◽  
Wenyuan Xu
Keyword(s):  
Mobile Devices ◽  
Error Rate ◽  
Proof Of Concept ◽  
Equal Error Rate ◽  
Time Intervals ◽  
Heterogeneous Sensors ◽  
Verification Scheme ◽  
Hotel Room ◽  
Human Interactions ◽  
Time Autonomous

Indoor proximity verification has become an increasingly useful primitive for the scenarios where access is granted to the previously unknown users when they enter a given area (e.g., a hotel room). Existing solutions either rely on homogeneous sensing modalities shared by two parties or require additional human interactions. In this article, we propose a context-based indoor proximity verification scheme, called SenCS, to enable real-time autonomous access for mobile devices, utilizing the available heterogeneous sensors at the user side and at the room side. The intuition is that only when the user is within a room can sensors from both sides observe the same events in the room. Yet such a solution is challenging, because the events may not provide enough entropy within the required time and the heterogeneity in sensing modalities may not always agree on the sensed events. To overcome the challenges, we exploit the time intervals between successively human actions to create heterogeneous contextual fingerprints (HCF) at a millisecond level. By comparing the contextual similarity between the HCF s from both the room and user sides, SenCS accomplishes the indoor proximity verification. Through proof-of-concept implementation and evaluations on 30 participants, SenCS achieves an accuracy of 99.77% and an equal error rate (EER) of 0.23% across various hardware configurations.

Detection of Microbubbles Using the Hough Transform

2013 ◽  
Vol 378 ◽  
pp. 478-482
Author(s):  
Yoshihiro Mitani ◽  
Toshitaka Oki
Keyword(s):  
Image Processing ◽  
Error Rate ◽  
Hough Transform ◽  
Detection Method ◽  
Experimental Results ◽  
Average Error ◽  
Image Processing Techniques ◽  
Average Error Rate ◽  
Processing Techniques

The microbubble has been widely used and shown to be effective in various fields. Therefore, there is an importance of measuring accurately its size by image processing techniques. In this paper, we propose a detection method of microbubbles by the approach based on the Hough transform. Experimental results show only 4.49% of the average error rate of the undetected microbubbles and incorrectly detected ones. This low percentage of the error rate shows the effectiveness of the proposed method.

Verification of the feed-forward control for a piezoelectric actuator using differential flatness approach

2018 ◽  
Vol 30 (3) ◽  
pp. 438-444
Author(s):  
Jomah Alzoubi ◽  
Shadi A Alboon ◽  
Amin Alqudah
Keyword(s):  
Mobile Devices ◽  
Piezoelectric Actuator ◽  
Experimental Results ◽  
Control Voltage ◽  
Differential Flatness ◽  
Digital Cameras ◽  
Precise Positioning ◽  
Feed Forward ◽  
Feed Forward Control ◽  
Micro Technology

In the last decade, the applications of nano- and micro-technology are widely used in many fields. In the modern mobile devices, such as digital cameras, there is an increased demand to achieve fast and precise positioning for some parts such as the recording sensor. Therefore, a smart material (piezoelectric) is used to achieve this requirement. This article discusses the feed-forward control for a piezoelectric actuator using differential flatness approach. The differential flatness approach is used to calculate the required voltage to control the piezoelectric actuator movement. The control voltage will be applied to the real actuator. The simulation and experimental results are compared for the actuator. The aim of this article is to verify the feed-forward control for second eigenfrequency using the differential flatness approach for the piezoelectric actuator.

scholarly journals A Review on Efficient Identification of Posture Alterations Through the Analysis of the Footprint and Artificial Vision Technologies

2021 ◽  
Vol 20 ◽  
pp. 15-24
Author(s):  
Taha Ahmadi ◽  
Hernández Cristian ◽  
Cubillos Neil
Keyword(s):  
Mobile Devices ◽  
Mobile Applications ◽  
Visual Analysis ◽  
Computer Programs ◽  
Experimental Results ◽  
Artificial Vision ◽  
Portable Devices

This article presents a review of the most relevant manual techniques and technologies developed from the field of artificial vision aimed at identifying biomechanical alterations. The purpose is to describe the most important aspects of each technology, focused on the description of each of its stages and experimental results, which suggest the integration of mobile devices with artificial vision techniques, in addition to the different computer programs used for such end. Finally, the results showed that the identification of the crook index for alterations in posture turns out to be a technique currently used by most specialists. The great challenge is to develop portable devices through mobile applications that allow the detection of the corvo index and the barometric analysis, as well as for other types of applications that depend on visual analysis by experts.

scholarly journals A User Authentication Enabled Piezoelectric Force Touch System for the Internet of Things

2020 ◽  
Author(s):  
Anbiao Huang ◽  
Shuo Gao ◽  
Arokia Nathan
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Internet Of Things ◽  
Error Rate ◽  
User Authentication ◽  
The Internet ◽  
Equal Error Rate ◽  
High User ◽  
Iot Applications ◽  
The Internet Of Things

In Internet of Things (IoT) applications, among various authentication techniques, keystroke authentication methods based on a user’s touch behavior have received increasing attention, due to their unique benefits. In this paper, we present a technique for obtaining high user authentication accuracy by utilizing a user’s touch time and force information, which are obtained from an assembled piezoelectric touch panel. After combining artificial neural networks with the user’s touch features, an equal error rate (EER) of 1.09% is achieved, and hence advancing the development of security techniques in the field of IoT.

scholarly journals YAICD: Yet Another IMSI Catcher Detector in GSM

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Parimah Ziayi ◽  
Seyed Mostafa Farmanbar ◽  
Mohsen Rezvani
Keyword(s):  
Mobile Devices ◽  
Cellular Networks ◽  
Mobile Networks ◽  
Experimental Results ◽  
Lte Networks ◽  
Backward Compatibility ◽  
Man In The Middle ◽  
Base Transceiver Station ◽  
Gsm Network

In GSM, the network is not authenticated which allows for man-in-the-middle (MITM) attacks. Attackers can track traffic and trace users of cellular networks by creating a rogue base transceiver station (BTS). Such a defect in addition to the need for backward compatibility of mobile networks makes all GSM, UMTS, and LTE networks susceptible to MITMs. These attacks are conducted using IMSI-Catchers (ICs). Most of the solutions proposed for detecting ICs in the literature are based on using specific mobile devices with root access. Also, they cannot identify ICs to which users are not connected. In this paper, we propose an approach called YAICD for detecting ICs in the GSM network. YAICD consists of a sensor that can be installed on Android mobile devices. It detects ICs by extracting 15 parameters from signals received from BTSs. We also established a lab-scale testbed to evaluate YAICD for various detection parameters and for comparing it against existing solutions in the literature. The experimental results show that YAICD not only successfully detects ICs using the parameters but also identifies ICs to which users are not yet connected to the network.

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